ILOG CPLEX 11.0 User's Manual

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Once the model is created, solving it and querying the solution is straightforward. Whatremains to be pointed out is the exception handling. In case of an error, ILOG CPLEX willthrow an exception of type IloException or one of its subclasses. Thus the entireILOG CPLEX program is enclosed in try/catch statements. The InputDataReader canthrow exceptions of type java.io.IOException orInputDataReader.InputDataReaderException.Since none of these three possible exceptions is handled elsewhere, the main function endsby catching them and issuing appropriate error messages.The call to the method cplex.end frees the memory that ILOG CPLEX uses.The entire source code listing for the example is available as Diet.java in the standarddistribution at yourCPLEXinstallation/examples/src.Modifying the ModelAn important feature of ILOG CPLEX is that you can modify a previously created model toconsider different scenarios. Furthermore, depending on the optimization model andalgorithm used, ILOG CPLEX will save as much information from a previous solution aspossible when optimizing a modified model.The most important modification method is IloModel.add, for adding modeling objects tothe active model. Conversely, you can use IloModel.remove to remove a modeling objectfrom a model, if you have previously added that object.When you add a modeling object such as a ranged constraint to a model, all the variablesused by that modeling object implicitly become part of the model as well. However, whenyou remove a modeling object, no variables are implicitly removed from the model. Instead,variables can only be explicitly removed from a model by calling IloMPModeler.delete.(The interface IloMPModeler derives from the class IloModel, among others. It isimplemented by the class IloCplex.) This call will cause the specified variables to bedeleted from the model, and thus from all modeling objects in the model that are using thesevariables. In other words, deleting variables from a model may implicitly modify othermodeling objects in that model.The API of specific modeling objects may provide modification methods. For example, youcan change variable bounds by using the methods IloNumVar.setLB andIloNumVar.setUB. Similarly, you can change the bounds of ranged constraints by usingIloRange.setLB and IloRange.setUB.Because not all the optimizers that implement the IloModeler interface support the abilityto modify a model, modification methods are implemented in IloMPModeler. Thesemethods are for manipulating the linear expressions in ranged constraints and objectivefunctions used with IloCplex. The methods IloMPModeler.setLinearCoef,94 ILOG CPLEX 11.0 — USER’ S MANUAL
IloMPModeler.setLinearCoefs, and IloMPModeler.addToExpr apply in thissituation.The type of a variable cannot be changed. However, it can be overwritten for a particularmodel by adding an IloConversion object, which allows you to specify new types forvariables within that model. When ILOG CPLEX finds a conversion object in the activemodel, it uses the variable types specified in the conversion object instead of the originaltype specified for the optimization. For example, in a model containing the following lines,ILOG CPLEX will only generate solutions where variable x is an integer (withintolerances), yet the type returned by x.getType will remain IloNumVarType.Float.IloNumVar x = cplex.numVar(0.0, 1.0);cplex.add(cplex.conversion(x, IloNumVarType.Int));A variable can be used only in at most one conversion object, or the model will no longer beunambiguously defined. This convention does not imply that the type of a variable can bechanged only once and never again after that. Instead, you can remove the conversion objectand add a new one to implement consecutive variable type changes. To remove theconversion object, use the method IloModel.remove.ILOG CPLEX 11.0 — USER’ S MANUAL 95
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ILOG CPLEX 11.0User’s ManualSepte
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C O N T E N T STable of ContentsILO
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Building the Model by Column . . .
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Program Description . . . . . . . .
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Cholesky Factor in the Log File . .
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Examples: QCP . . . . . . . . . . .
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Chapter 18 Using Piecewise Linear F
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Representing the Data. . . . . . .
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Reading and Writing MPS Files . . .
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Protected Variables in Presolve Red
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P R E F A C EMeet ILOG CPLEXILOG CP
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When a linear optimization problem
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◆◆◆◆implemented in ILOG CPL
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Solution Pool: Generating and Keepi
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Table 1ExamplesExample Source File
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◆◆◆◆◆◆Overview of the A
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Wolsey, Laurence A., Integer Progra
Page 43 and 44: C H A P T E R1ILOG Concert Technolo
Page 45 and 46: Compiling and LinkingCompilation an
Page 47 and 48: The class IloNumVar provides method
Page 49 and 50: IloModel object itself and added to
Page 51 and 52: Table 1.1 Concert Technology Modeli
Page 53 and 54: Choosing an OptimizerSolving the ex
Page 55 and 56: Table 1.4 on page 55 summarizes the
Page 57 and 58: Dynamic control of the solution pro
Page 59 and 60: However, querying solution values v
Page 61 and 62: parameter for the getQuality method
Page 63 and 64: model2.add(model1);model2.add(IloCo
Page 65 and 66: Example: Optimizing the Diet Proble
Page 67 and 68: Concert Technology, as demonstrated
Page 69 and 70: method IloModel::add returns the mo
Page 73 and 74: Licenses in a Java ApplicationILOG
Page 75 and 76: Models that consist only of such co
Page 77 and 78: The special case of linear expressi
Page 79 and 80: IloObjective obj = cplex.add(cplex.
Page 81 and 82: Solving the ModelOnce you have crea
Page 83 and 84: IloCplex cplex = new IloCplex();Ilo
Page 85 and 86: IloCplex.setParam(IloCplex.IntParam
Page 87 and 88: Thus, the suggested method for sett
Page 89 and 90: Basis InformationWhen solving an LP
Page 91 and 92: where A is a sparse matrix. A spars
Page 93: The example starts by evaluating th
Page 99 and 100: ◆What is the purpose (the objecti
Page 101 and 102: Step 3Create the modelGo to the com
Page 103 and 104: Step 9Add nutritional constraintsGo
Page 105 and 106: Step 14Display the solutionGo to th
Page 107 and 108: Step 18Enclose the application in t
Page 109 and 110: C H A P T E R4ILOG CPLEX Callable L
Page 111 and 112: ◆◆file reading and writing rout
Page 113 and 114: ◆◆If data already exist in MPS,
Page 115 and 116: For example, let’s look at the sy
Page 117 and 118: As you modify a problem object thro
Page 119 and 120: However, ILOG CPLEX updates the nam
Page 121 and 122: whether an optimization should be a
Page 123 and 124: Callable Library have names greater
Page 125 and 126: ◆CPXsetstrparam accepts arguments
Page 127 and 128: Here’s another way to visualize a
Page 129 and 130: program—can discover the length o
Page 131: Part IIProgramming ConsiderationsTh
Page 134 and 135: ◆ Test Data on page 135◆ Choose
Page 136 and 137: ◆ Use the MIP optimizer if the pr
Page 138 and 139: Choose Clarity First, Efficiency La
Page 140 and 141: 1. If you can reproduce the behavio
Page 142 and 143: indices, errors will occur. Therefo
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Understanding File FormatsThe refer
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definitions that it finds. The firs
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◆◆IloXmlReader creates a reader
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Controlling Message ChannelsIn both
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more calls to the message handling
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Concert Technology Message Channels
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Types of ILM Runtime LicensesILM ru
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char *inststr = NULL;char *envstr =
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The registerLicense Method for Java
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not correct that problem either. In
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In the Interactive Optimizer, you c
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For models not in the current worki
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◆◆In the .NET API, pass an inst
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C H A P T E R9Solving LPs: Simplex
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The symbolic names for these settin
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When no candidates are present, the
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solution; but examination of soluti
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Then to later read an advanced basi
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Table 9.5 PPriInd Parameter Setting
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ILOG CPLEX ignores the coefficients
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Numeric DifficultiesILOG CPLEX is d
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esult is the same as would be obtai
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automatically perturbs the variable
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4. Consider alternate scalings.You
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smaller in absolute value than the
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exceptions are caught as well, and
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C H A P T E R10Solving LPs: Barrier
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The ILOG CPLEX Barrier Optimizer is
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And then you call the solution rout
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Here is an example of a log file fo
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If solution values are large in abs
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Normalized errors, for example, rep
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◆ workmem in the Interactive Opti
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est order. It may require more time
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choice of starting-point heuristic
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To see the current value of the col
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C H A P T E R11Solving Network-Flow
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◆◆l a , the lower bound, sets t
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of the objective function calculate
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then ILOG CPLEX will carry out such
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-a 1 + a 2 - a 8 - a 9 + a 14 = 0-
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C H A P T E R12Solving Problems wit
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convex QPs, Q must be positive semi
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A similar Java program using Concer
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◆ qp indicates that you want ILOG
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RootAlg parameter (QPMETHOD in the
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ilolpex1.cpp counterpart. Here the
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C H A P T E R13Solving Problems wit
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Figure 13.2y(0, 1)(-1, 0)d(1, 0)xc(
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Detecting Problem TypeILOG CPLEX de
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COLUMNSMARK0000 'MARKER''INTORG'C15
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C180 R100 159C180 R119 200C180 R120
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QMATRIXC158 C158 1C158 C189 0.5C189
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◆From the Callable Library, use t
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Part IVDiscrete OptimizationThis pa
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Stating a MIP ProblemA mixed intege
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sections does not matter. To enter
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◆milp, miqp, or miqcpindicating t
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1. finding a succession of improvin
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easonable amount of computation tim
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If the solution to the relaxation s
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anches taken so far in this dive. S
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directives about the order in which
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◆ Parameters Affecting Cuts on pa
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◆●●IloCplex.getNcuts in the J
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Relaxation Induced Neighborhood Sea
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Table 14.10Parameters for Controlli
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Library). If this process succeeds,
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After you have solved a MIP, you wi
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Table 14.13Settings of the MIP Disp
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ILOG CPLEX also logs its addition o
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◆ Parent specifies the NodeID of
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parameter settings are also worth c
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When you set a MIP cutoff value, IL
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memory. It also includes several op
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◆◆●●at problem modification
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◆In the Callable Library, use the
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●●Then call CPXprimopt to optim
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◆ The Incumbent and the Solution
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c17: x1 - y11 >= 0c18: x1 - y21 >=
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Populating the Solution PoolILOG CP
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NodesCuts/Node Left Objective IInf
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Note: The parameter to limit the nu
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Example: Using Populate after MIP O
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Limitations Due to Numeric Difficul
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Examining the Solution PoolIn the I
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For a sample of these methods or ro
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◆◆In the Callable Library (C AP
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Then display the objective value of
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◆ If you want to filter solutions
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●●●In the C++ API, use the me
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NAME locationRNGFILTER f2 -inf 0tra
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continuous, a model containing one
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◆The routine setPriorities sets t
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What Are Semi-Continuous Variables?
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With that model, now the applicatio
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Piecewise Linearity in ILOG CPLEXSo
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Reminder: It may help you understan
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overlaps with an endpoint of two ot
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Figure 18.4400003000020000100000Fig
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in demand; in terms of this model,
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348 ILOG CPLEX 11.0 — USER’ S M
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What Are Logical Constraints?For IL
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◆◆Cplex.NotCplex.IfThenAgain, t
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In Concert Technology applications,
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Describing the ProblemThe problem i
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Notice that how much to use and buy
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Then use a for-loop to add the cons
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These lines (the action of the if-s
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Describing the ProblemThe problem i
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Stating Precedence ConstraintsIn ea
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Solving the ProblemAn emphasis on f
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What Is Column Generation?In colloq
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Solving this model with all columns
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Likewise, the application adds an a
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Pattern Generator ModelThe submodel
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Complete ProgramYou can see the ent
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your problem formulation caused thi
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To control the types of reductions
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What Is Unboundedness?Any class of
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Callable Library use the advanced r
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the conflict to arrive at a minimal
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the IIS finder can, and often more.
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Then you will see results like thes
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Now view the entire conflict with t
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The constraints in conflict with th
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If a model contains more than one c
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Immediately after that statement, i
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◆ Groups in the Conflict Refiner
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The infeasibility on which FeasOpt
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Now the following lines invoke Feas
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suggests decreasing the upper bound
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C H A P T E R28User-Cut and Lazy-Co
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However, there is an important dist
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◆◆Through the routine CPXreadco
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Here is an example of an MPS file e
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C H A P T E R29Using GoalsThis chap
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How Goals Are Implemented in Branch
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In the Java API, a Fail goal is ret
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This return statement returns an An
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The Goal StackTo understand how goa
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soon as they are enclosed in a goal
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The global cut goal for lhs[i] ≤
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If a node has multiple evaluators a
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As this example is an extension of
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C H A P T E R30Using Optimization C
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Reference Documents about Informati
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Query or Diagnostic CallbacksQuery
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● Cplex.DisjunctiveCutCallback in
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◆◆●●Cplex.HeuristicCallback
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It is not customary to write such a
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IloCplex::Callback mycallback = cpl
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Here is the previous sample of code
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◆◆cbdata, a pointer to ILOG CPL
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conventions of the Interactive Opti
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een added, it can be deleted by cal
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Interaction Between Callbacks and I
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C H A P T E R31Goals and Callbacks:
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The only functionality that is not
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C H A P T E R32Advanced Presolve Ro
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once. All of the node relaxation so
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- 5x1, + x2 ≤ 0 2y1, + y2, ≥ 1x
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problem, the problem has a presolve
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Modifying a ProblemThis section bri
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C H A P T E R33Advanced MIP Control
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value strictly greater than one. It
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Cut CallbackThe next example we con
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problem. Since branching involves a
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C H A P T E R34Parallel OptimizersT
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Threads and Performance Considerati
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3. Call the parallel optimizer with
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optimizer turns out to be the faste
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0 0 3383.7784 16 4505.0000 Cuts: 35
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I N D E XIndexAabsolute objective d
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ole in converting LP to network flo
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emoving from basis (C++ API) 62repr
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CPX_PARAM_PCPX_PARAM_POLISHTIMEsolu
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CPXcreateprob 467CPXcreateprob rout
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CPXwcpxwarning message channel 151C
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empty goal 431, 435end methodIloEnv
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getDuals method (Java API) 88getMax
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emove method (Java API) 94IloModele
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ecords singularities 188relocating
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from 218head 218sink 219source 219s
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control callbacks and 453query call
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primal reduction 385primal simplex
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eturn statusBounded (Java API) 81Er
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solvingdiet problem (Java API) 82mo
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arrier 208WorkMem 293WorkMem parame
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